Means and methods for determining risk of type-1 diabetes by serum protein biomarkers

ABSTRACT

The present invention relates to methods for predicting a risk of a subject for Type 1 diabetes (T1D) on the basis of expression levels of protein markers in a sample obtained from the subject. The present invention also relates to in vitro kits for use in said methods.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/FI2015/050448 filed on Jun. 18, 2015 and claims priority to FinnishApplication No. 20140186 filed in Finland on Jun. 18, 2014. The entirecontents of all of the above applications are hereby incorporated byreference.

FIELD OF THE INVENTION

The present invention relates to the field of molecular diagnostics.More specifically the present invention relates to means and methods forpredicting a risk of a subject for Type 1 diabetes (T1D).

BACKGROUND OF THE INVENTION

The measurement of islet cell autoantibodies is currently the principlemeans of identifying an emerging threat in developing Type 1 diabetes.The risks associated with the appearance of islet antibodies have beenevaluated in depth, and overall the appearance of multiple biochemicallydefined autoantibodies correlates with progression to diseaseirrespective of genetic risk group or autoantibody combination. T1D isgenerally diagnosed at the point when the majority of the insulinproducing beta cells in the pancreas have been destroyed, at which stagethe patient is dependent on insulin supplements for the rest of theirlife. Methods to establish risk and potential onset are thus needed togain insight in the disease etiology and design potentialtreatment/prevention strategies.

Proteomic analyses in the study of T1D have previously mostly addresseddifferences in sera from diabetic patients and non-diabetic subjects.Whilst in-depth comparisons of proteins in samples from healthy subjectsand patients with T1D have distinguished the diseased state, theidentification of changes preceding this aggressive autoimmune diseaseis important for disease prediction and prevention. Such markers couldbe used in the evaluation of risks and preventative treatments.

BRIEF DESCRIPTION OF THE INVENTION

The present disclosure is directed to methods and kits that useful foridentifying the risk that an individual, particularly an individualhaving a HLA-conferred risk for type 1 diabetes, will develop type 1diabetes (T1D). The risk can be determined based on amounts of one ormore of the protein markers disclosed herein.

In one aspect, the present disclosure is directed to a method ofpredicting, determining and/or monitoring a risk of and/or progressiontowards Type 1 Diabetes (T1D) in an individual. The method comprises thesteps of

a) determining a protein marker profile in a sample obtained from theindividual, said profile comprising:

-   -   i) PROF1 and FLNA,    -   ii) SHBG,    -   iii) AFAM and APOC4, or    -   iv) IBP2, ADIPO, and CO2,

b) comparing the determined protein marker profile and a correspondingcontrol profile, and

c) responsive to the comparison, determining a prediction correspondingto a relative risk of the individual developing T1D or a stage ofprogression towards T1D.

In some embodiments, the profile of i) further comprises one or moreprotein markers selected from the group consisting of VASP, CFL1, ACTB,TAGL2 and TMSB4X. In some other embodiments, the profile of ii) furthercomprises one or more protein markers selected from the group consistingof CO8G, ZPI, BTD, CO5, and THBG. In some further embodiments, theprofile of iv) further comprises one or both protein markers selectedfrom SHBG, CO8G. In some still further aspects, any of the markerprofiles set forth above may further comprise any one or more proteinmarkers selected from those listed in Table 4 and/or Table 5.

According to some embodiments, the above-defined profiles of i) to iii)are used to predict the risk of T1D prior to seroconversion.

In some embodiments, the individual whose risk for developing T1D has aHLA-conferred risk for T1D.

In another aspect, the present disclosure is directed to an in vitroscreening kit comprising one or more testing agents for testing abiological sample for a protein marker profile indicative of a risk ofdeveloping T1D, wherein said profile comprises:

i) PROF1 and FLNA;

ii) SHBG;

iii) AFAM and APOC4; or

iv) IBP2, ADIPO, and CO2.

In some embodiments, the kit comprises one or more testing agents whichrecognize the protein markers of profile i) and one or more furthertesting agents which recognize one or more protein markers selected fromthe group consisting of TAGL2, VASP, CFL1, ACTB, and TMSB4X.

In some other embodiments, the kit comprises one or more testing agentswhich recognize the protein marker of profile ii) and one or morefurther testing agents which recognize one or more protein markersselected from the group consisting of CO8G, ZPI, BTD, CO5, and THBG.

In some still other embodiments, the kit comprises one or more testingagents which recognize the protein marker of profile iv) and one or morefurther testing agents which recognize one or both protein markersselected from the group consisting of SHBG and CO8G.

In some further embodiments, the kit may comprise, in addition to theone or more testing agents set forth above, one or more further testingagents which recognize one or more protein markers listed in Table 4and/or Table 5.

Other objectives, aspects, embodiments, details and advantages of thepresent invention will become apparent from the following figures,detailed description, examples, and dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following the invention will be described in greater detail bymeans of preferred embodiments with reference to the attached drawings,in which

FIG. 1 is a schematic presentation of the study design that depicts themanner in which the discovery was made. Using a prospective longitudinalserum sample collection from children with a HLA-conferred risk for type1 diabetes, samples were selected on the basis of clinical outcome andthe determined levels of type 1 diabetes associated autoantibodies.Serum samples were prepared for proteomics analysis by massspectrometry. Comparisons were made between children that developed type1 diabetes and their age, risk and gender matched controls. Twoquantitative approaches were employed. Firstly with isotope taggedrelative and absolute quantitative (iTRAQ) reagents, secondly using alabel free approach.

FIG. 2 shows timing of the serum samples used, represented relative tothe first detection of T1D-associated autoantibodies in years. Thesamples profiled for the T1D-developing children are represented byblack diamonds. These include iTRAQ measurements (13 pairs) and labelfree measurements in two batches (LFQ1 and LFQ2, n=6 and 9 pairsrespectively). For the comparison between children (healthy vs. type 1diabetes developing) the analyses considered protein abundancethroughout the series (184 vs. 184 age-matched samples), samples before(71 vs. 71 age-matched samples), and after detection of seroconversion(113 vs. 113 age-matched samples).

FIG. 3 is a schematic representation of the 8-plex iTRAQ labellingstrategy. Pool reference channels were used to link iTRAQ comparisonsbetween experiments.

FIG. 4A shows classification between T1D-developing subjects andage-matched controls on the basis of the abundance of APOC4 and AFAM.The top scoring pairs method was used, yielding a 91% success rate.Black triangles represent the controls and open squares represent the T1D-developing children (i.e. cases).

FIG. 4B illustrates relative abundance measurements for APOC4 and AFAMfor the case and control subjects.

FIG. 5A illustrates receiver-operator characteristics on the basis ofthe differences between APOC4 and AFAM levels and the individual effectof these proteins alone. The combination of APOC4 and AFAM gave an areaunder the curve (AUC) of 0.89.

FIG. 5B illustrates APOC4 levels in cases and controls, beforeseroconversion and in antibody positive subjects vs. age matchedcontrols.

FIG. 6 illustrates the receiver-operator characteristic (ROC) curve ofthe abundance of PROF1 and FLNA as detected three to six months prior toautoantibody seroconversion.

FIG. 7 illustrates the outcome of pathway analysis of differentiallyabundant proteins three to six months prior to autoantibodyseroconversion, emphasizing the putative importance of these proteins inevents preceding seroconversion. The abbreviations used in the figurerefer to genes encoding the listed proteins.

FIG. 8A illustrates the receiver-operator characteristic (ROC) curve ofthe abundance of SHBG throughout the time series (AUC 0.74), while FIG.8B illustrates SHBG vs. CO8G, ZPI, BTD, CO5, and THBG (AUC=0.80).

FIG. 9 illustrates the receiver-operator characteristic (ROC) curve ofthe abundance of SHBG vs IBP2, ADIPO, CO2, and CO8G (AUC=0.85) postseroconversion.

DETAILED DESCRIPTION OF THE INVENTION

In some implementations, the present disclosure is directed to methods,proteomic profiles and kits useful for determining or predicting therisk that an individual will develop Type 1 diabetes (T1D), ordetermining an individual's stage of progression towards T1D, ormonitoring or predicting an individual's progression towards T1D. As isdescribed, this risk can be assessed based on the profiles of severalpanels of protein markers whose applicability, in some embodiments,depends on whether known T1D autoantibodies have been detected.

In some important embodiments, a prediction of an individual's risk ofdeveloping T1D can be made prior to any signs of seroconversion. As usedherein, the term “seroconversion” refers to the first detection of oneor several T1D-associated autoantibodies against beta cell-specificantigens in serum. These include islet cell specific autoantibodies(ICA), insulin autoantibodies (IAA), glutamic acid decarboxylase 65autoantibodies (GADA), islet antigen-2 autoantibodies (IA-2A), and zinctransporter 8 autoantibodies (ZnT8A). In some embodiments, the followingcut-off values may be used for determining the presence or absence ofthe autoantibodies: ICA≥4 JDFU (Juvenile Diabetes Foundation units),IAA≥3.48 RU (relative units), GADA≥5.36 RU, IA-2A≥0.43 RU, andZnT8A≥0.61 RU. Seroconversion may occur years, e.g. 1 to 2 years, beforeclinical diagnosis.

Typically, the individual whose risk for T1D is to be determined is ahuman subject, preferably a child or an adolescent. In some morepreferred embodiments, said subject does not show any signs ofseroconversion. As used herein, the terms “subject” and “individual” areinterchangeable.

The herein identified predictive proteomic profiles apply in particularto individuals having a Human Leukocyte Antigen (HLA)-conferred risk forT1D. As used herein, the term “HLA-conferred risk for T1D” refers to apredisposition to T1D as determined on the basis of the individual's HLAgenotype. In some embodiments, HLA-conferred susceptibility is assignedif the individual carries HLA-DQB1 alleles *02/*0302 or *0302. In theexperiments conducted, T1D diagnosed individuals whose risk wasHLA-conferred were compared with control subjects with the samesusceptibility. Similarly in the implementation of this screen, controland pooled control reference may be employed.

As used herein, the term “proteomic profile” refers to a set of proteinsin a biological sample at a given time. A proteomic profile changescontinually in response to internal and external events. Thus, aproteomic profile may be used not only to predict an individual's riskof developing a disease at a given time but also to monitor any changesin the prediction or disease status e.g. in response to treatment ordietary or other changes in the individual's lifestyle. The term may beused interchangeably with the terms “protein expression profile”,“protein signature”, “protein marker profile”, and the like, as isevident to a person skilled in the art. Predictive protein markersidentified herein include those listed in Table 1.

TABLE 1 Protein markers comprised in various protein marker profiles ofthe present disclosure Protein Abbreviation Protein name Protein ID ACTBActin P60709 ADIPO Adiponectin Q15848 AFAM Afamin P43652 APOC4Apolipoprotein C-IV P55056 BTD Biotinidase P43251 CFL1 Cofilin-1 P23528CO2 Complement 2 P06681 CO5 Complement 5 P01031 CO8G Complementcomponent P07360 C8 gamma chain FLNA Filamin-A P21333 IBP2 Insulin-likegrowth fac- P18065 tor-binding protein 2 PROF1 Profilin-1 P07737 SHBGSex hormone-binding P04278 globulin TAGL2 Transgelin-2 P37802 THBGThyroxine-binding globu- P05543 lin TMSB4X Thymosin beta-4 P62328 VASPVasodilator-stimulated P50552 phosphoprotein ZPI Z-dependent proteaseQ9UK55 inhibitor

As used herein, the term “determining a protein marker profile”, and thelike, refers to detecting, measuring, or otherwise assessing the level,amount, or proportion of one or more protein markers belonging to agiven profile. Said determining may give a relative or absolute valuerepresenting the amount or level of said marker in a biological sampleobtained from an individual whose marker profile and, eventually, therisk of developing T1D, are to be determined.

Suitable biological samples for use in accordance with the presentdisclosure include, but are not limited to, tissue samples (e.g.pancreatic samples), blood samples including whole blood, serum, plasma,peripheral blood mono-nuclear cells and any purified blood cell type,and urine samples. In essence, any biological protein-containing samplemay be used for the present purposes.

The level or amount of a protein marker in a biological sample may bedetermined by a variety of techniques as is readily apparent to askilled person. Nonlimiting examples of suitable methods include massspectrometry-based quantitative proteomics techniques, such as isobaricTags for Relative and Absolute Quantification reagents (iTRAQ) and labelfree analysis, as well as selected reaction monitoring (SRM) massspectrometry. Also, the level or amount of a protein marker may bedetermined by e.g. an immunoassay, spectrophotometry, an enzymaticassay, an ultraviolet assay, a kinetic assay, an electrochemical assay,a colorimetric assay, a turbidimetric assay, an atomic absorption assay,flow cytometry, or any combination thereof. Further suitable analyticaltechniques include, but are not limited to, liquid chromatography suchas high performance/pressure liquid chromatography (HPLC), gaschromatography, nuclear magnetic resonance spectrometry, relatedtechniques and combinations and hybrids thereof, for example, a tandemliquid chromatography-mass spectrometry (LC-MS).

As used herein, the term “increased expression” refers to an increase inthe amount of a protein in a sample as compared with a correspondingcontrol sample. Said increase can be determined qualitatively and/orquantitatively according to standard methods known in the art. Theexpression is increased if the amount of the protein in the sample is,for instance, at least about 1.5 times, 1.75 times, 2 times, 3 times, 4times, 5 times, 6 times, 8 times, 9 times, time times, 10 times, 20times or 30 times the amount of the same protein in the control sample.

As used herein, the term “decreased expression” refers to a decrease inthe amount of a protein in a sample as compared with a correspondingcontrol sample. Said decrease can be determined qualitatively and/orquantitatively according to standard methods known in the art. Theexpression is decreased if the amount of the protein in the sample is,for instance, at least about 1.5 times, 1.75 times, 2 times, 3 times, 4times, 5 times, 6 times, 8 times, 9 times, time times, 10 times, 20times or 30 times lower than the amount of the same protein in thecontrol sample.

To determine whether the expression level or the amount of a proteinmarker is greater than or lower than normal, the normal amount of theprotein marker present in a biological sample from a relevant controlhas to be determined. Once the normal marker amounts are known, thedetermined marker amounts can be compared therewith and the significanceof the difference can be assessed using standard statistical methods.When there is a statistically significant difference between thedetermined marker amount and the normal amount, there is a significantrisk that the tested individual will develop T1D.

In some further embodiments, the levels, amounts or relative ratios ofone or more protein markers may be compared with a predeterminedthreshold value which is indicative of the risk of developing T1D.Statistical methods for determining appropriate threshold values will bereadily apparent to those of ordinary skill in the art. The thresholdvalue originates from a relevant control which may be a singleindividual not affected by T1D or be a value pooled from more than onesuch individual.

As used herein, the term “relevant control” refers to a control sample,control proteomic profile, or control value, preferably case matchedwith the individual whose risk for T1D is to be predicted. Case-matchingmay be made, for instance, on the basis of one of more of the followingcriteria: age, date of birth, place of birth, gender, predisposition forT1D, HLA status and any relevant demographic parameter. In someembodiments, said control sample or profile consists of a pool of,preferably case-matched, relevant control samples or profiles. In someembodiments, said control prolife or control value has beenpredetermined prior to predicting a risk of T1D in an individual inaccordance with the present disclosure. In some other embodiments,determining said control profile or control value may be comprised as amethod step in any of the predictive methods disclosed herein.

Optionally, before to be compared with the control sample, the proteinmarker levels are normalized using standard methods.

Receiver Operation Characteristic (ROC) curves may be utilized todemonstrate the trade-off between the sensitivity and specificity of amarker, as is well known to skilled persons. The sensitivity is ameasure of the ability of the marker to detect the disease, and thespecificity is a measure of the ability of the marker to detect theabsence of the disease. The horizontal X-axis of the ROC curverepresents 1-specificity, which increases with the rate of falsepositives. The vertical Y-axis of the curve represents sensitivity,which increases with the rate of true positives. Thus, for a particularcutoff selected, the values of specificity and sensitivity may bedetermined. In other words, data points on the ROC curves represent theproportion of true-positive and false-positive classifications atvarious decision boundaries. Optimum results are obtained as thetrue-positive proportion approaches 1.0 and the false-positiveproportion approaches 0.0. However, as the cutoff is changed to increasespecificity, sensitivity usually is reduced and vice versa.

As used herein, the term “false positive” refers to a test result whichclassifies an unaffected subject incorrectly as an affected subject,i.e. a subject having or predicted to develop a disease. Likewise,“false negative” refers to a test results which classifies a subject whohas or will develop a disease incorrectly as an unaffected subject.

As used herein, the term “true positive” refers to a test result whichclassifies a subject who has or will develop a disease correctly as asubject having or predicted to develop a disease. Likewise, “truenegative” refers to a test result which classifies an unaffected subjectcorrectly as an unaffected.

In accordance with the above, the term “success rate” refers to thepercentage-expressed proportion of affected individuals with a positiveresult, while the terms “false positive rate” and “false detection rate”(FDR) refer to the percentage-expressed proportion of unaffectedindividuals with a positive result.

The area under the ROC curve, often referred to as the AUC, is a measureof the utility of a marker in the correct identification of diseasesubjects, i.e. subjects who will develop T1D. Thus, the AUC values canbe used to determine the effectiveness of the test. An area of 1.0represents a perfect test; an area of 0.5 represents a worthless test. Atraditional rough guide for classifying the accuracy of a diagnostic orpredictive test is the following: AUC values 0.9 to 1.0 represent a testwith excellent diagnostic or predictive power, AUC values 0.80 to 0.90represent a test with good diagnostic or predictive power, AUC values0.70 to 0.80 represent a test with fair diagnostic or predictive power,AUC values 0.60 to 0.70 represent a test with poor diagnostic orpredictive power, and AUC values 0.50 to 0.60 represent a test withfailed diagnostic or predictive power.

As described in the experimental part, the present disclosure is basedon an analysis on a unique series of prospective serum samples fromat-risk children collected in the Finnish Type 1 Diabetes Prediction andPrevention (DIPP) study. The samples covered the time span from beforethe development of autoantibodies (seroconversion) through the diagnosisof diabetes. Healthy, persistently autoantibody-negative childrenmatched for date and place of birth, gender and the HLA-DQB1-conferredgenetic risk were chosen as controls. Children who eventually developedT1D are herein sometimes referred to as “cases”.

Quantitative analyses of the data resulted in identification of proteinsthat may be used to predict T1D risk and provide indications of diseaseonset prior to diagnosis and, surprisingly, even prior toseroconversion. The identified predictive protein marker panels may begrouped into three different categories on the basis of their temporalutility in predicting T1D risk.

A first identified temporal category of protein markers is based onchanges in protein abundance prior to seroconversion, particularly 3 to6 months prior to seroconversion. The expression of these proteinseither increases or decreases before seroconversion. As is described inthe Examples below, analyses of the receiver operator characteristics ofthe data provided good classification of the children en route to T1Dwith respect to their controls. To be more specific, in samplescollected within a 6 months' time period prior to seroconversion,increased levels of PROF1 were observed with a consistency sufficientfor observing an AUC of 0.85 in the ROC analysis. Further analysisrevealed that the combination of PROF1 and FLNA provided an AUC of 0.88(FIG. 6).

Thus, in an embodiment of the present invention, T1D risk prediction,preferably prior to seroconversion, is based on determining the proteinmarker profile of PROF1 and FLNA, and comparing the determined proteinmarker profile with that of a control. Increased expression of theprotein marker profile with respect to the control marker profile isindicative of a risk of getting T1D.

Pathway analysis of differentially abundant proteins three to six monthsprior to autoantibody seroconversion revealed biochemical associationsbetween PROF1, FLNA, TAGL2, VASP, CFL1, ACTB, and TMSB4X (FIG. 7). Thus,PROF1 and FLNA based classification of at-risk subjects and unaffectedsubjects may be improved by including any one of proteins markers TAGL2,VASP, CFL1, ACTB, and TMSB4X, either alone or in any combination, in theprotein marker profile to be determined for predicting the risk of T1D.

A second identified temporal category of protein markers is based onchanges in protein abundance, as compared to control levels, throughoutthe time series. In other words, these markers may be used forpredicting the risk of T1D either before or after seroconversion. Theexpression of these proteins is either increased or decreased across thetime series.

A first protein marker panel belonging to the second temporal categorycomprises SHBG either alone or in any combination with one or more ofprotein markers CO8G, ZPI, BTD, CO5, and THBG. According to the presentresults, the expression of SHBG was decreased while the expression ofCO8G, ZPI, BTD, CO5, and THBG was increased in the T1D-developingchildren as compared with the expression levels in the control samples.On its own, SHBG gave an AUC of 0.74 but when used in combination withany one of CO8G, ZPI, BTD, CO5, and THBG, the AUC was improved to theorder of 0.8 (FIGS. 8A and 8B).

Thus, in an embodiment of the present invention, T1D risk prediction isbased on determining the protein marker profile of SHBG either alone inany combination with one or more of protein markers selected from thegroup consisting of CO8G, ZPI, BTD, CO5, and THBG, and comparing thedetermined protein marker profile with that of a control. Differences inthe protein marker profile with respect to the control marker profileare indicative of a risk of developing T1D. These marker profiles aresuitable for being utilized either in a time period prior toseroconversion or from the seroconverted state to diagnosis.

A second protein marker panel belonging to the second temporal categorycomprises APOC4 and AFAM. As is described in the Examples below,statistical analysis of clinical samples revealed that APOC4 and AFAM,preferably in combination, are protein markers remarkably effective indetermining the risk of T1D with clinically acceptable detection andfalse positive rates. Herein, APOC4 and AFAM were consistently detectedat disparate levels, lower and higher, respectively, in prospectivesamples of children who developed T1D. Taken together, a success rate of91% with a false positive rate of 5% on the basis of APOC4 together withAFAM levels was determined (FIG. 4A). The ROC analysis of APOC4 incombination with AFAM gave an AUC of 0.87 (FIG. 5A).

Thus, in an embodiment of the present invention, T1D risk prediction isbased on determining the protein marker profile of APOC4 and AFAM, andcomparing the determined protein marker profile with that of a control.Differences in the protein marker profile with respect to the controlmarker profile are indicative of a risk of developing T1D.

A third identified temporal protein marker panel comprises proteinswhose expression levels reflect the transition of the seroconvertedstate towards diagnosis. The present data analyses revealed thatprogression to T1D is characterized by decreasing IBP2 and ADIPO, andincreasing CO2. Thus, seroconverted patients should be monitored forthese protein markers as indicators of pre-disease severity and/or toidentify any need for intervention, moreover, when taken together withthe contrast provided together with SHBG and CO8G (FIG. 9).

Thus, in an embodiment of the present invention, T1D risk prediction isbased on determining the protein marker profile of IBP2, ADIPO, and CO2,and comparing the determined protein marker profile with that of acontrol. Differences in the protein marker profile with respect to thecontrol marker profile are indicative of a risk of developing T1 D. In afurther embodiment, said marker profile includes, in addition to IBP2,ADIPO, and CO2, either one or both of SHBG and CO8G.

In some embodiments, the above-defined protein marker panels may be usedin any desired combination. Thus, a protein marker panel comprisingPROF1 and FLNA and, optionally, at least one of TAGL2, VASP, CFL1, ACTB,and TMSB4X, may be used in combination with a protein marker panelcomprising SHBG and, optionally at least one of CO8G, ZPI, BTD, CO5, andTHBG, or with a protein marker panel comprising APOC4 and AFAM, or witha protein marker panel comprising IBP2, ADIPO, and CO2 and, optionally,one or both of SHBG and CO8G. Likewise, in some embodiments, a proteinmarker panel comprising SHBG and, optionally at least one of CO8G, ZPI,BTD, CO5, and THBG, may be used in combination with a protein markerpanel comprising APOC4 and AFAM, or with a protein marker panelcomprising IBP2, ADIPO, and CO2, and optionally one or both of SHBG andCO8G. Likewise, a protein marker profile comprising APOC4 and AFAM maybe used in combination with a protein marker panel comprising IBP2,ADIPO, and CO2, and optionally one or both of SHBG and CO8G.

In some embodiments, assessing or predicting an individual's risk forT1D may be based on determining, in addition to the protein markerprofiles set forth above, the level or amount of one or more proteinmarkers set forth in Table 4 and/or Table 5 The selection of one or moreof these additional protein markers to be used may depend on a varietyof practical considerations such as availability of protein markertesting reagents or equipment.

In summary, using mass spectrometry based analysis of immuno-depletedsera, the present inventors have demonstrated for the first time serumproteomics profiles of the pre-diabetic transition all the way to thediagnosis, comparing profiles between children progressing to type 1diabetes and healthy children. These results demonstrate shared andgroup specific longitudinal changes against a back-ground of widesubject heterogeneity, suggesting that components of the moderatelyabundant serum proteins may be indicative of the emerging threat of type1 diabetes.

Optionally, the present methods may further comprise determiningvariations in the individual's proteomic profile at different timepoints in order to monitor, preferably prior to seroconversion, anychanges in the development of the risk for T1D.

In some implementations, the present methods of predicting anindividual's risk for T1 D may further include therapeutic intervention.Once an individual is identified to have an increased risk for T1D,he/she may be subjected to, for instance, dietary or other changes inthe individual's lifestyle.

In some further implementations, the herein identified panels ofpredictive protein markers may be used for screening new therapeutics orpreventive drugs for T1D. In other words, the present panels may be usedfor assessing whether or not a candidate drug is able to correct theprotein marker profile of an at-risk individual towards that of anunaffected individual. For example, individuals identified to have anincreased risk for T1D on the basis of their protein marker profilebelonging to either the first or second temporal category of predictiveprotein markers could be employed as targets in preventive vaccinationtrials or in trials aimed for identifying preventive agents, such asprobiotics, for T1D.

In some still further embodiments and implementations, the hereinidentified panels of protein markers may be used for determining anindividual's stage of progression towards T1D and/or for monitoring orpredicting an individual's progression towards T1D. Said stage ofprogression may be a pre-seroconversion stage, e.g. 3 to 6 monthspre-seroconversion or 9 to 12 months pre-seroconversion, or apost-seroconversion stage, e.g. 3 to 6 months pre-diagnosis, 9 to 12months pre-diagnosis, or 15 to 18 months pre-diagnosis. Of the hereindisclosed panels of protein markers, panel i), i.e. PROF1 and FLNA,optionally, with one or more protein markers selected from the groupconsisting of TAGL2, VASP, CFL1, ACTB, and TMSB4X, is particularlysuitable for monitoring said individual's T1D progression at thepre-seroconversion stage and predicting progression to thepost-seroconversion stage, e.g. such that said individual is predictedto face the post-seroconversion stage e.g. within 9 to 12 months or 3 to6 months. The protein marker panel ii), i.e. SHBG, optionally, with oneor more protein markers selected from the group consisting of CO8G, ZPI,BTD, CO5, and THBG, and/or the protein marker panel iii), i.e. AFAM andAPOC4, may also be used as set forth above regarding the panel i) but,alternatively or in addition, they may also be used for monitoring saidindividual's T1D progression at the post-seroconversion stage orpredicting progression to T1D diagnosis, e.g. such that said individualis predicted to face the T1D diagnosis e.g. within 9 to 12 months or 3to 6 months. The panel iv), i.e. IBP2, ADIPO, and CO2, optionally, withone or both of SHBG and CO8G, is particularly suitable for monitoringsaid individual's T1D progression at the post-seroconversion stage orpredicting progression to T1D diagnosis, e.g. such that said individualis predicted to face the T1D diagnosis e.g. within 9 to 12 months or 3to 6 months.

In some still further implementations, the present disclosure relates toan in vitro kit for predicting, preferably before seroconversion, a riskof a subject for developing T1D. The kit may be used in any one of themethods of the present disclosure. Typically, the kit comprises one ormore testing agents for testing a sample obtained from an individualwhose risk for T1D is to be determined for any one or more of theprotein marker panels disclosed herein indicative of a risk ofdeveloping T1 D. In some embodiments, the kit may comprise one or moretesting agents which recognize a protein marker profile comprising:

i) PROF1 and FLNA, and, optionally, also one or more protein markersselected from the group consisting of TAGL2, VASP, CFL1, ACTB, andTMSB4X;

ii) SHBG and, optionally, also one or more protein markers selected fromthe group consisting of CO8G, ZPI, BTD, CO5, and THBG;

iii) AFAM and APOC4; or

iv) IBP2, ADIPO, and CO2 and, optionally, one or both of SHBG and CO8G.

In some embodiments, the kit may also comprise testing agents for anyprotein marker, or a combination thereof, listed in Table 4 or 5.

The kit may also comprise a computer readable medium comprisingcomputer-executable instructions for performing any method of thepresent disclosure.

It will be obvious to a person skilled in the art that, as technologyadvances, the inventive concept can be implemented in various ways. Theinvention and its embodiments are not limited to the examples describedbelow but may vary within the scope of the claims.

EXAMPLES Example 1. Research Design and Methods

A schematic of the experimental design used in the identification ofthese markers is illustrated in FIG. 1. Detailed description of theproteomic measurements, samples and comparisons are provided in Example2.

Subjects and Sample Collection

All children studied were participants in the Finnish DIPP study, wherechildren identified as at risk for type 1 diabetes based on their HLAgenotype were followed prospectively from birth. Venous non-fastingblood samples were collected from the children at each study visit. Serawere separated and stored at −70° C. within three hours from the bloodsample collections. Serum islet cell autoantibodies (ICA) measurementswere made as previously described. For ICA positive children, glutamicacid decarboxylase (GADA), tyrosine phosphatase-related proteinantibodies (IA-2A), and insulin antibodies (IAA) were also determined.

Proteomic measurements were made on sera from 28 case children whodeveloped type 1 diabetes during DIPP follow-up. Prospective serumsamples (5-11 per child) were selected to represent different phases ofT1D progression from autoantibody-negativity to seroconversion (SC) andfurther until the overt disease. A persistently autoantibody negativecontrol child was matched with each case (typically in the order ofseven samples per child), based on the date and place of birth, genderand HLA-DQB1 genotype. The prospective control serum samples werematched with the case samples by age at sample draw. Altogether 409serum samples were analyzed, of which sera from twenty-six children (13case-control pairs) were processed for iTRAQ analysis, and from thirtychildren (15 case-control pairs) for analysis using a label freeapproach (Table 2, FIG. 2).

TABLE 2 Summary of the children progressing to T1D whose samples werestudied with proteomics Number of Age at samples serocon- Age atanalyzed HLA-DQB1 version diagnosis (case and Analysis ID Gender riskalleles (years) (years) control) Method D1 M *02, *03:02 1.3 2.2 7 & 7iTRAQ D2 M *02, *03:02 1.4 4.0 7 & 7 iTRAQ D3 M *02, *03:02 1.3 3.9 7 &7 iTRAQ D4 F *02, *03:02 1.5 3.3 7 & 7 iTRAQ D5 F *02, *03:02 3.4 7.0 7& 7 iTRAQ D6 F *02, *03:02 0.5 4.0 7 & 7 iTRAQ D7 F *02, *03:02 0.6 4.17 & 7 iTRAQ D8 M *02, *03:02 1.0 4.4 7 & 7 iTRAQ D9 M *02, *03:02 2.53.6 7 & 7 iTRAQ D10 M *03:02, x 1.5 2.5 7 & 7 iTRAQ D11 M *03:02, x 1.34.0 11 & 9  iTRAQ D12 M *03:02, x 2.0 2.2 5 & 7 iTRAQ D13 M *03:02, x3.5 5.5 7 & 6 iTRAQ D14 M *02, *03:02 6.1 8.8 7 & 8 LFQ D15 M *02,*03:02 2.6 8.3 6 & 7 LFQ D16 F *02, *03:02 1.0 10.0 6 & 6 LFQ D17 F *02,*03:02 5.0 7.7 7 & 7 LFQ D18 M *03:02, x 1.3 12.1 6 & 8 LFQ D19 F*03:02, x 1.3 8.6 7 & 8 LFQ D20 M *03:02, x 2.5 6.6 9 vs. 9 LFQ D21 M*02, *03:02 4.0 12.5 9 vs. 8 LFQ D22 F *02, *03:02 2.0 7.8 9 vs. 8 LFQD23 F *02, *03:02 1.0 3.6 7 vs 7 LFQ D24 F *02, *03:02 1.4 8.6 8 vs 9LFQ D25 F *02, *03:02 6.5 9.3 9 vs 8 LFQ D26 F *02, *03:02 2.2 9.2 7 vs.7 LFQ D27 M *03:02, x 3.0 4.3 8 vs 6 LFQ D28 M *02, *03:02 1.3 3.8 8 vs8 LFQ x ≠ *02, *03:01, *06:02/3

See also Tables 3a b & c in Example 2 for more information on the casesand their matched controls.

Sample Preparation

Serum samples were depleted of the most abundant proteins usingimmuno-affinity columns. Beckman-Coulter (IgY-12) and Agilent (Hu14)columns were used in this study, where the same depletion method wasalways applied to follow-up samples of each case-control pair.

Samples from twenty-six children were compared using the iTRAQ method,using twenty-seven paired/cross referenced 8-plex ITRAQ labellingschemes of the samples. For iTRAQ labelling, the samples were thenprocessed in accordance with the manufacturer's protocol for 8-plexreagents (ABSciex, Framingham, Mass., USA), then fractionated usingstrong cation exchange chromatography. The typical labelling scheme forthe iTRAQ measurements was as indicated in FIG. 3, where a pooledreference was used to link experiments.

Samples from thirty children were compared using label freequantification (LFQ), with concentration and digestion performed in asimilar manner as the iTRAQ samples, with the digests otherwiseunfractionated prior to LC-MS/MS. Two batches of analysis were made,namely LFQ1 (n=6 pairs) and LFQ2 (n=9 pairs).

LC-MS/MS Analysis

HPLC-tandem mass spectral analyses (LC-MS/MS) were made with aQSTAR-Elite time of flight instrument (TOF) and an Orbitrap-VelosFourier transform (FT) instrument. For the analysis of iTRAQ labelledsamples, the collision induced dissociation and higher-energycollisional dissociation modes were used to record positive ion tandemmass spectra for the QSTAR-Elite and Orbitrap-Velos, respectively. Thelabel-free data were acquired with the Orbitrap-Velos using collisioninduced dissociation. Chromatographic separations were made with 150mm×75 μm i.d. tapered columns packed with Magic C18-bonded silica(200A), using binary gradients of water and acetonitrile with 0.2%formic acid.

LC-MS/MS Data Processing

The iTRAQ data were analyzed using ProteinPilot™ software using theParagon™ identification algorithm with a Human Swiss-Prot database (Aug.18, 2011, 20245 entries). The database searches were made in thoroughmode specifying 8-plex-iTRAQ quantification, trypsin digestion and MMTSmodification of cysteine. ITRAQ ratios were calculated relative to thereference samples using ProteinPilot™.

The label-free data were analyzed with Proteome Discover version 1.3(Thermo Scientific) together with Mascot (2.1, Matrix Science). Thedatabase search criteria were trypsin digestion, MMTS modification ofcysteine, deamidation of N/Q and methionine oxidation, using the samedatabase. The mass tolerance settings of 5 ppm for the precursors and0.5 Da for fragments were used. For quantitative analysis, Pro-genesissoftware (version 4.0) was used for feature detection and alignment ofthe ion maps and calculation of intensity-based abundance measurementsfor each protein. To facilitate comparison of the label free data withthe iTRAQ results, the intensity values of each protein was scaledrelative to the median intensity of each protein across the pairedcase-control sample series.

Data Analysis

Serum proteomics differences between healthy children and thoseprogressing to type 1 diabetes. Case-control abundance ratios werecalculated for the paired samples. The ratios were log₂ transformed andused in rank product analysis (RP) as is well known in the art toidentify differences either throughout the time series (n=28), prior tothe detection of autoantibody seroconversion (n=23) and prior todiagnosis (n=28). The RP analyses were made with 10,000 timespermutations, and a false discovery rate (FDR) of less than or equal to5% was applied (Benjamini-Hochberg (BH) correction). The differencesanalyzed were made for the average case-control ratios at the followingtime intervals (selected on the basis of the similarity of the sampleseries used): throughout the time series; prior to seroconversion, i.e.3 to 6 months, 9 to 12 months and the overall period; prior todiagnosis, i.e. 3 to 6 months, 9 to 12 months, within 1.5 years andoverall. Additional statistical tests were made using the WilcoxonRank-sum test.

Serum proteomics changes in children progressing to type 1 diabetes.Spearman's rank correlation analyses were made to assess whether any ofthe protein profiles were correlated with the emergence of T1D. Theanalyses were made for the collected case—control abundance ratios ofthe paired samples. For this comparison there were eleven well matchedpairs with samples before and after seroconversion (subjects D1, D4, D5,D9, D10, D11, D12, D14, D15, D17, D19). The analysis was repeatedseparately for the case and control subjects to reference ratios. Tounify this analysis the age/time axis was scaled between birth anddiagnosis (zero to one). A Spearman's correlation coefficient greaterthan 0.4 was considered as a valid weak correlation (p-value based on10,000 permutations of the time axis. BH corrected FDR≤0.05).

Subject and status classification. The top scoring pairs (TSP) methodwas applied to identify whether combinations of the quantified proteinscould classify the samples and subjects. The leave-one-out method wasused for cross validation. The method was tested in terms of relativeprotein abundance throughout the time series, before or afterseroconversion, as well as for the intra individual longitudinal changesputatively associated with seroconverted status. The within subjectaveraged log₂ protein relative abundance values were used for the timeperiods studied. In the preliminary data (ITRAQ and LFQ1) APOC4 wasdetected in 16 out of 19 subject pairs, the 16 were separately analyzedwith the TSP method. The failure to quantify APOC4 in all children wasattributed to differences in instrument performance rather than itsabsence.

Example 2. Supplemental Information on Samples

Subjects and Samples

Details of serum samples obtained children progressing to type 1diabetes (D) and their matched controls (C) are shown in Tables 3a, 3b,and 3c below.

TABLE 3a iTRAQ samples HLA- Time HLA- Case DQB1 Time from Control DQB1Age at Sample risk Age at from T1D Ab + Sample risk sample/ name Genderalleles sample SC diagnosis ve name Gender alleles days D1_1 Male 02/193 −274 683 0 C1_1 Male 02/ 193 D1_2 0302 284 −183 592 0 C1_2 302 288D1_3 375 −92 501 0 C1_3 367 D1_4 467 0 409 1 C1_4 465 D1_5 584 117 292 1C1_5 557 D1_6 719 252 157 1 C1_6 743 D1_7 807 340 69 1 C1_7 932 D2_1Male 02/ 122 −407 −1396 0 C2_1 Male 02/ 279 D2_2 0302 529 0 −989 1 C2_2302 455 D2_3 724 195 −794 1 C2_3 552 D2_4 940 411 −578 1 C2_4 742 D2_51143 614 −375 1 C2_5 914 D2_6 1234 705 −284 1 C2_6 1107 D2_7 1446 917−72 1 C2_7 1470 D3_1 Male 02/ 278 −195 −1145 0 C3_1 Male 2/ 278 D3_20302 740 267 −683 0 C3_2 302 559 D3_3 1004 531 −419 1 C3_3 636 D3_4 1110637 −313 1 C3_4 722 D3_5 1146 673 −277 1 C3_5 925 D3_6 1308 835 −115 1C3_6 1092 D3_7 1423 950 0 1 C3_7 1286 D4_1 Female 02/ 183 −370 −1016 0C4_1 Female 02/ 190 D4_2 0302 299 −254 −900 0 C4_2 302 370 D4_3 453 −100−746 0 C4_3 453 D4_4 553 0 −646 0 C4_4 547 D4_5 645 92 −554 1 C4_5 644D4_6 756 203 −443 1 C4_6 730 D4_7 1199 646 0 1 C4_7 1192 D5_1 Female201/ 87 −1169 −2462 0 C5_1 Female 201/ 92 D5_2 0302 547 −709 −2002 0C5_2 302 567 D5_3 907 −349 −1642 0 C5_3 902 D5_4 1073 −183 −1476 0 C5_41092 D5_5 1256 0 −1293 0 C5_5 1296 D5_6 2187 931 −362 1 C5_6 2184 D5_72549 1293 0 1 C5_7 2571 D6_1 Male 201/ 673 479 −899 1 C6_1 Male 201/ 549D6_2 0302 769 575 −803 1 C6_2 302 637 D6_3 944 750 −628 1 C6_3 727 D6_41047 853 −525 1 C6_4 913 D6_5 1271 1077 −301 1 C6_5 1090 D6_6 1364 1170−208 1 C6_6 1273 D6_7 1450 1256 −122 1 C6_7 1452 D7_1 Female 201/ 733532 −753 1 C7_1 Female 201/ 659 D7_2 0302 833 632 −653 1 C7_2 302 735D7_3 964 763 −522 1 C7_3 910 D7_4 1087 886 −399 1 C7_4 1099 D7_5 1188987 −298 1 C7_5 1275 D7_6 1265 1064 −221 1 C7_6 1464 D7_7 1402 1201 −841 C7_7 1653 D8_1 Male 201/ 1018 646 −643 1 C8_1 Male 201/ 640 D8_2 03021102 730 −559 1 C8_2 302 730 D8_3 1207 835 −454 1 C8_3 900 D8_4 1318 946−343 1 C8_4 1101 D8_5 1410 1038 −251 1 C8_5 1255 D8_6 1486 1114 −175 1C8_6 1465 D8_7 1590 1218 −71 1 C8_7 1640 D9_1 Male 201/ 102 −795 −1206 0C9_1 Male 201/ 103 D9_2 0302 233 −664 −1075 0 C9_2 302 273 D9_3 392 −505−916 0 C9_3 366 D9_4 567 −330 −741 0 C9_4 525 D9_5 654 −243 −654 0 C9_5614 D9_6 897 0 −411 0 C9_6 923 D9_7 1086 189 −222 1 C9_7 1100 D10_1 Male302 118 −446 −882 0 C10_1 Male 302 109 D10_2 201 −363 −799 0 C10_2 204D10_3 280 −284 −720 0 C10_3 293 D10_4 371 −193 −629 0 C10_4 397 D10_5663 99 −337 1 C10_5 670 D10_6 731 167 −269 1 C10_6 733 D10_7 927 363 −731 C10_7 922 D11_1 Male 302 98 −363 −1372 0 C11_1 Male 302 89 D11_2 188−273 −1282 0 C11_2 208 D11_3 289 −172 −1181 0 C11_3 292 D11_4 370 −91−1100 0 C11_4 360 D11_5 461 0 −1009 0 C11_5 454 D11_6 548 87 −922 0C11_6 542 D11_7 743 282 −727 0 C11_7 738 D11_8 902 441 −568 0 C11_8 929D11_9 1179 718 −291 1 C11_9 1272 D11_10 1261 800 −209 1 C11_10 1456D11_11 1476 1015 6 1 D12_1 Male 302 160 −553 −731 0 C12_1 Male 302 132D12_2 349 −364 −542 0 C12_2 302 D12_3 531 −182 −360 0 C12_3 386 D12_4713 0 −178 1 C12_4 473 D12_5 807 94 −84 1 C12_5 665 C12_6 739 C12_7 903D13_1 Female 302 271 −1022 −1739 0 C13_1 Female 302 293 D13_2 363 −930−1647 0 C13_2 398 D13_3 488 −805 −1522 0 C13_3 494 D13_4 729 −564 −12810 C13_4 721 D13_5 917 −376 −1093 0 C13_5 910 D13_6 1897 604 −113 1 C13_61834 D13_7 2009 716 −1 1

TABLE 3b Label-free samples LFQ1 HLA- Time HLA- Case DQB1 Time fromControl DQB1 Age at Sample geno- Age at from T1D ICA, Sample geno-sample/ name Gender type sample SC diagnosis JDFU name Gender type daysD14_1 Male  02, 88 −2135 −3120 0 C14_1 Male  02, 97 D14_2 0302  1841−382 −1367 0 C14_2 0302  1787 D14_3 2055 −168 −1153 0 C14_3 2045 D14_42383 160 −825 1 C14_4 2417 D14_5 2824 601 −384 1 C14_5 2827 D14_6 2915692 −293 1 C14_6 3009 D14_7 3206 983 −2 1 C14_7 3184 D15_1 Male  02, 104−839 −2927 0 C15_1 Male  02, 85 D15_2 0302  655 −288 −2376 0 C15_2 0302 638 D15_3 746 −197 −2285 0 C15_3 735 D15_4 2105 1162 −926 1 C15_4 2148D15_5 2491 1548 −540 1 C15_5 2490 D15_6 2636 1693 −395 1 C15_6 2668D16_1 Female  02, 284 −84 −3366 0 C16_1 Female  02, 272 D16_2 0302  3680 −3282 0 C16_2 0302  359 D16_3 2930 2562 −720 1 C16_3 2759 D16_4 31122744 −538 1 C16_4 3122 D16_5 3203 2835 −447 1 C16_5 3288 D16_6 3650 32820 1 C16_6 3645 D17_1 Female  02, 280 −1560 −2515 0 C17_1 Female  02, 259D17_2 0302  1469 −371 −1326 0 C17_2 0302  1485 D17_3 1644 −196 −1151 0C17_3 1641 D17_4 1840 0 −955 0 C17_4 1856 D17_5 1989 149 −806 1 C17_52015 D17_6 2197 357 −598 1 C17_6 2198 D17_7 2710 870 −85 1 C17_7 2739D18_1 Male 0302  368 −99 −4060 0 C18_1 Male 0302  387 D18_2 3133 2666−1295 1 C18_2 3108 D18_3 3304 2837 −1124 1 C18_3 3297 D18_4 3513 3046−915 1 C18_4 3485 D18_5 4235 3768 −193 1 C18_5 4207 D18_6 4332 3865 −961 C18_6 4396 D19_1 Female 0302  116 −355 −3008 0 C19_1 Female 0302  126D19_2 285 −186 −2839 0 C19_2 279 D19_3 374 −97 −2750 0 C19_3 378 D19_42418 1947 −706 0 C19_4 2378 D19_5 2577 2106 −547 0 C19_5 2567 D19_6 27472276 −377 0 C19_6 2742 D19_7 2948 2477 −176 1 C19_7 2931

TABLE 3c Label-free samples LFQ2 HLA- Time HLA- Case DQB1 Time fromControl DQB1 Age at Sample geno- Age at from T1D ICA, Sample geno-sample/ name Gender type sample SC diagnosis JDFU name Gender type daysD20_1 Male *03:02, 179 −736 −2227 0 C20_1 Male *03:02, 191 D20_2 x 650−265 −1756 0 C20_2 x 638 D20_3 748 −167 −1658 0 C20_3 714 D20_4 915 0−1491 1 C20_4 910 D20_5 1811 896 −595 1 C20_5 1816 D20_6 2022 1107 −3841 C20_6 1995 D20_7 2210 1295 −196 1 C20_7 2191 D20_8 2330 1415 −76 1C20_8 2359 D20_9 2406 1491 0 1 C20_9 2557 D21_1 Male *02, 89 −1362 −44770 C21_1 Male *02, 91 D21_2 *03:02 1262 −350 −3465 0 C21_2 *03:02 1282D21_3 1101 −189 −3304 0 C21_3 1449 D21_4 1451 0 −3115 1 C21_4 3836 D21_53875 2424 −691 1 C21_5 4024 D21_6 4049 2598 −517 1 C21_6 4200 D21_7 42542803 −312 1 C21_7 4382 D21_8 4344 2893 −222 1 C21_8 4564 D21_9 4525 3074−41 1 D22_1 Female *02, 94 −642 −2736 0 C22_1 Female *02, 187 D22_2*03:02 183 −553 −2647 0 C22_2 *03:02 550 D22_3 561 −175 −2269 0 C22_3647 D22_4 645 −91 −2185 0 C22_4 733 D22_5 736 0 −2094 1 C22_5 2189 D22_62220 1484 −610 1 C22_6 2385 D22_7 2416 1680 −414 1 C22_7 2543 D22_8 25701834 −260 1 C22_8 2748 D22_9 2753 2017 −77 1 C22_9 2915 D22_10 2829 2093−1 1 D23_1 Female *02, 96.00 −283 −1205 0 C23_1 Female *02, 122 D23_2*03:02 181.00 −198 −1120 0 C23_2 *03:02 278 D23_3 281.00 −98 −1020 0C23_3 466 D23_4 482.00 103 −819 1 C23_4 586 D23_5 580.00 201 −721 1C23_5 1111 D23_6 1104.00 725 −197 1 C23_6 1306 D23_7 1301.00 922 0 1C23_7 1663 D24_1 Female *02, 97.00 −358.00 −3032.00 0 C24_1 Female *02,87 D24_2 *03:02 293.00 −162.00 −2836.00 0 C24_2 *03:02 275 D24_3 371.00−84.00 −2758.00 0 C24_3 369 D24_4 455.00 0.00 −2674.00 1 C24_4 452 D24_52491.00 2036.00 −638.00 1 C24_5 2524 D24_6 2731.00 2276.00 −398.00 1C24_6 2699 D24_7 2834.00 2379.00 −295.00 1 C24_7 2888 D24_8 3032.002577.00 −97.00 1 C24_8 3063 D24_9 3129.00 2674.00 0.00 1 C24_9 3247D25_1 Female *02, 184.00 ####### −3224.00 0 C25_1 Female *02, 277 D25_2*03:02 279.00 ####### −3129.00 0 C25_2 *03:02 1495 D25_3 1479.00 −902.00−1929.00 0 C25_3 1831 D25_4 1847.00 −534.00 −1561.00 0 C25_4 2034 D25_52017.00 −364.00 −1391.00 0 C25_5 2162 D25_6 2220.00 −161.00 −1188.00 0C25_6 2517 D25_7 2577.00 196.00 −831.00 1 C25_7 2706 D25_8 2745.00364.00 −663.00 1 C25_8 2874 D25_9 3398.00 1017.00 −10.00 1 D26_1 Female*02, 88.00 −699.00 −3280 0 C26_1 Female *02, 71 D26_2 *03:02 467.00−320.00 −2901 0 C26_2 *03:02 450 D26_3 555.00 −232.00 −2813 0 C26_3 546D26_4 3074.00 2287.00 −294 1 C26_4 3089 D26_5 3140.00 2353.00 −228 1C26_5 3283 D26_6 3224.00 2437.00 −144 1 C26_6 3446 D26_7 3326.00 2539.00−42 1 C26_7 3656 D27_1 Male *03:02, 101.00 −980.00 −1450.00 0 C27_1 Male*03:02, 97 D27_2 x 738.00 −343.00 −813.00 0 C27_2 x 705 D27_3 901.00−180.00 −650.00 0 C27_3 1089 D27_4 1091.00 10.00 −460.00 1 C27_4 1384D27_5 1194.00 113.00 −357.00 1 C27_5 1559 D27_6 1277.00 196.00 −274.00 1C27_6 1740 D27_7 1369.00 288.00 −182.00 1 D27_8 1465.00 384.00 −86.00 1D28_1 Male *02, 188.00 −301.00 −1184.00 0 C28_1 Male *02, 169 D28_2*03:02 307.00 −182.00 −1065.00 0 C28_2 *03:02 267 D28_3 405.00 −84.00−967.00 0 C28_3 454 D28_4 656.00 167.00 −716.00 1 C28_4 677 D28_5 774.00285.00 −598.00 1 C28_5 740 D28_6 1153.00 664.00 −219.00 1 C28_6 915D28_7 1244.00 755.00 −128.00 1 C28_7 1104 D28_8 1364.00 875.00 −8.00 1C28_8 1280Sample ComparisonsPaired Samples

The comparisons for the samples were mostly paired within a time windowof 60 days, with but a few exceptions where a later sample was used. Thesamples where no pairing could be made were only used to gain anoverview in the clustering of the expression profiles. For the analysisof selected time points the following grouping was made (based on theuse of the paired case/control ratios) according to the ages and timeintervals in Table 3a and Table 3b: The samples were grouped on thebasis of collection time

i) 3 to 6 months pre-seroconversion;

ii) 9 to 12 months pre-seroconversion;

iii) 3 to 6 months pre-diagnosis;

iv) 9 to 12 months pre-diagnosis;

v) 15 to 18 months pre-diagnosis;

vi) Throughout the 18 months pre-diagnosis

Example 3. Results

The iTRAQ measurements detailed on average the quantitative comparisonof 220 proteins, and in total 658 proteins were identified andquantified with two or more unique peptides. In comparison to referenceconcentrations and after excluding depletion targets these spanned arange of estimated concentrations of six orders of magnitude. With theanalyses using a label free approach, 261 proteins were consistentlydetected and quantified with more than one unique peptide and spanned asimilar dynamic range of detection. There were 248 proteins common tothe two techniques.

Differences Between the Serum Proteomes of Children Who Developed T1Dand their Age Matched Controls

For the subjects considered in this study, the children who developedT1D had lower levels of SHBG, APOC4, APOC2, and APOC1 than theirage-matched healthy controls (FDR<3%) (Table 3).

In samples before seroconversion, lower levels of APOC4 and APOC2 andAPOA were apparent in children developing type 1 diabetes than in theircontrols. Similarly, specific consideration of the samples 3 to 6 monthsprior to seroconversion was consistent with the lower levels of bothIBP2, TSP4, APOC2 and APOC4, as well as a larger relative abundance ofPROF1, FLNA, and TAGL2.

With similar analysis of the age matched post seroconversion data,several proteins were distinguished with a lower relative abundance,including SHBG, ADIPO, THBG, APOC4, APOC2, IBP2, APOC1, and APOC3(FDR<5%, Table 4).

TABLE 4 Serum proteins detected at different levels between childrenprogressing to type 1 diabetes and their matched controls Entry ID nameProtein names col q-value P55056 APOC4 Apolipoprotein C-IV a−, b−, d−,e−, 0.000 g−, h−, j− P02655 APOC2 Apolipoprotein C-II a−, b−, d−, e−,0.001 g−, h−, j− P18065 IBP2 Insulin-like growth factor-binding protein2 d−, e−, j−, g− 0.009 P11226 MBL2 Mannose-binding protein C a−, e−, g−,j− 0.011 P07737 PROF1 Profilin-1 b+, c+, d+, h+ 0.000 P04278 SHBG Sexhormone-binding globulin a−, e−, g−, j− 0.002 P02654 APOC1Apolipoprotein C-I a−, e−, j−, g− 0.031 P02656 APOC3 ApolipoproteinC-III a−, j−, g− 0.013 Q96KN2 CNDP1 Beta-Ala-His dipeptidase a+, e+, j+0.017 P21333 FLNA Filamin-A a+, d+, h+ 0.000 P00740 FA9 Coagulationfactor IX g+, j+ 0.004 P02751 FINC Fibronectin b−, g+ 0.009 P05154 IPSPPlasma serine protease inhibitor g+, j+ 0.007 P12955 PEPD Xaa-Prodipeptidase d−, h− 0.003 P32119 PRDX2 Peroxiredoxin-2 h−, e+ 0.005P37802 TAGL2 Transgelin-2 d+, h+ 0.000 Q15848 ADIPO Adiponectin e− 0.000P08519 APOA Apolipoprotein(a) h− 0.000 Q15582 BGH3 Transforming growthfactor-beta-induced e− 0.019 protein ig-h3 P43251 BTD Biotinidase g+0.017 P04003 C4BPA C4b-binding protein alpha chain e+ 0.013 P02748 CO9Complement component C9 e+ 0.010 P09172 DOPO Dopamine beta-hydroxylasee+ 0.000 Q9NPH3 IL1AP Interleukin-1 receptor accessory protein c+ 0.030Q08380 LG3BP Galectin-3-binding protein e+ 0.012 P43121 MUC18 Cellsurface glycoprotein MUC18 d− 0.049 Q6UXB8 PI16 Peptidase inhibitor 16e− 0.018 Q15063 POSTN Periostin e− 0.005 P06702 S10A9 Protein S100-A9 i−0.001 P02743 SAMP Serum amyloid P-component e+ 0.049 P35443 TSP4Thrombospondin-4 d− 0.043 Q9UK55 ZPI Protein Z-dependent proteaseinhibitor i+ 0.036 a) Throughout, n = 28, b) Pre-seroconversion (PS), n= 23, c) 9 to 12 months PS, d) 3 to 6 months PS, e) Post seroconversionand <1.5 year pre-diagnosis (PD), n = 28, f) 15 to 18 months PD, n = 28,g) 3 to 6 months PD, n = 20, h) 3 to 12 months PS. i) at diagnosis, j)Post seroconversion (all)Longitudinal Changes in the Serum Proteome of Children En Route to T1D

With the analysis of protein abundance ratios there were no significantcorrelations observed between the case-control ratios with time towardsdiagnosis. On the contrary, the case and control reference correlationsgave a much clearer indication of the longitudinal changes in the serumproteome in both case and control. Distinct from the correlated proteinsobserved with both the case and control children (>0.4, FDR<0.05) werechanges in the abundance of 26 proteins (14 increased and 12 decreased,FDR<5%, Table 5). This included proteins reported in Table 4,emphasizing their utility in assessing advancement of the disease.

TABLE 5 Longitudinal changes in serum proteins specific toT1D-developing children Average Average Unique Unique Peptides Average %Correlation Entry Peptides Label Sequence coefficient Protein names nameEntry iTRAQ Free Coverage* (Spearman) Fetuin-B FETUB Q9UGM5 18 7 30 0.63Serum amyloid P-component SAMP P02743 32 9 37 0.51 Clusterin CLUS P1090970 35 43 0.50 C4b-binding protein alpha chain C4BPA P04003 21 11 24 0.49C4b-binding protein beta chain C4BPB P20851 5 2 20 0.48 Complementfactor I CFAI P05156 50 44 48 0.45 Inter-alpha-trypsin inhibitor ITIH4Q14624 251 92 63 0.44 heavy chain H4 Apolipoprotein C-IV APOC4 P55056 46 22 0.44 Insulin-like growth factor- IBP3 P17936 12 13 27 bindingprotein 3 Serum amyloid A-4 protein SAA4 P35542 5 10 22 0.43 Complementcomponent C8 CO8A P07357 51 35 38 0.43 alpha chain Complement C1qsubcomponent C1QB P02746 27 17 29 0.42 subunit B Hyaluronan-bindingprotein 2 HABP2 Q14520 20 12 26 0.40 Complement component C8 CO8G P0736028 7 58 0.40 gamma chain Transforming growth factor- BGH3 Q15582 17 1024 −0.41 beta-induced protein ig-h3 Ectonucleotide pyrophosphatase/ENPP2 Q13822 11 4 12 −0.41 phosphodiesterase family member 2 Poliovirusreceptor PVR P15151 5 4 8 −0.42 Vinculin VINC P18206 6 3 6 −0.42N-acetylmuramoyl-L-alanine PGRP2 Q96PD5 61 31 50 −0.42 amidaseContactin-1 CNTN1 Q12860 8 1 9 −0.43 L-lactate dehydrogenase B LDHBP07195 9 2 24 −0.46 chain 46) Extracellular superoxide SODE P08294 7 428 −0.48 dismutase [Cu-Zn] Apolipoprotein A-IV APOA4 P06727 189 97 74−0.54 Adiponectin ADIPO Q15848 13 3 33 −0.54 Neural cell adhesionmolecule 1 NCAM1 P13591 13 5 17 −0.60 Insulin-like growth factor- IBP2P18065 6 6 19 −0.64 binding protein 2

A subset of proteins was identified were the absolute Spearman'scorrelation coefficient was greater than 0.4 (FDR<0.05) and not observedat or above these thresholds in the control subjects. The analysis wasbased on the changes observed in eleven children representing thesamples before and after seroconversion (D1, D4, D5, D9, D10, D11, D12,D14, D15, D17, D19). The functional enrichment for these proteins isshow in Table 6. Notably included in this list were the proteins ADIPOand IBP2, which were also detected as differentially abundant in therank product analyses. The temporal change of these proteins could beused to assess increased risk, particularly in seroconverted subjects.

TABLE 6 GO annotations enriched in proteins increasing in the children,who progressed to type 1 diabetes Term P-Value Proteins FDR GO:0002526acute inflammatory response 3.8E−04 P07357, Q14624, P04003, P05156, 0.3P02746, P20851, P10909, P07360, P02743, P35542 GO:0019724 B cellmediated immunity  .6E−04 P07357, P04003, P05156, P02746, 1.0 P20851,P10909, P07360 GO:0006958 complement activation, classical 8.6E−04P07357, P04003, P05156, P02746, 1.0 pathway P20851, P10909, P07360GO:0016064 immunoglobulin mediated im- 8.6E−04 P07357, P04003, P05156,P02746, 1.0 mune response P20851, P10909, P07360 GO:0002455 humoralimmune response mediated 8.6E−04 P07357, P04003, P05156, P02746, 1.0 bycirculating immunoglobulin P20851, P10909, P07360 GO:0006954inflammatory response 0.0010 P07357, Q14624, P04003, P05156, 1.3 P02746,P20851, P10909, P07360, P02743, P35542 Complement pathway 0.0013 P07357,P04003, P05156, P02746, 1.3 P20851, P10909, P07360 GO:0002250 adaptiveimmune response 0.0014 P07357, P04003, P05156, P02746, 1.7 P20851,P10909, P07360 GO:0002449 lymphocyte mediated immunity 0.0014 P07357,P04003, P05156, P02746, 1.7 P20851, P10909, P07360 GO:0002460 adaptiveimmune response based 0.0014 P07357, P04003, P05156, P02746, 1.7 onsomatic recombination of immune recep- P20851, P10909, P07360 tors builtfrom immunoglobulin superfamily domains GO:0002443 leukocyte mediatedimmunity 0.0018 P07357, P04003, P05156, P02746, 2.1 P20851, P10909,P07360 GO:0006952 defense response 0.0032 P07357, Q14624, P04003,P05156, 3.8 P02746, P20851, P10909, P07360, P02743, P35542 Innateimmunity .0039 P07357, P04003, P05156, P02746, 3.8 P20851, P10909,P07360 GO:0006959 humoral immune response 0.0041 P07357, P04003, P05156,P02746, 4.8 P20851, P10909, P07360 GO:0002541 activation of plasmaproteins 0.0041 P07357, P04003, P05156, P02746, 4.8 involved in acuteinflammatory response P20851, P10909, P07360 GO:0002253 activation ofimmune response 0.0041 P07357, P04003, P05156, P02746, 4.8 P20851,P10909, P07360 GO:0006956 complement activation 0.0041 P07357, P04003,P05156, P02746, 4.8 P20851, P10909, P07360

Enrichment was calculated for proteins with an absolute Spearman'scorrelation coefficient of greater than 0.4 (FDR<0.05) that were notobserved at or above these thresholds in the control subjects. Abackground of the 208 proteins detected for these analyses was used inthe enrichment analysis. The protein names and detection details areindicated in Table 3.

Serum Proteomics Classification of the T1D-Developing Subjects

The top scoring pairs (TSP) method was applied to identify whethercombinations of the quantified proteins could classify the samples andsubjects. The leave-one-out method was used for cross validation.

TSP analysis for the subjects of iTRAQ and LFQ1 in which APOC4 wasquantified (16 of 19) classified the children progressing to type 1diabetes at a success rate of 91%; the area under the curve was 0.89.The classification was based on the combination of the relative levelsof APOC4 and AFAM, which were lower and higher than in the controls,respectively (FIGS. 4A and 5A).

TSP analysis for the LFQ2 subjects demonstrated that SHBG in combinationwith other proteins gave good classification of the subjects throughoutthe time series (up to 94% success rate). Analysis of the label freedata alone (LFQ1 and LFQ2) revealed that the combination of SHBG and BTDprovided an AUC of 0.85 for the post seroconversion data, and 0.77pre-seroconversion.

For the collected data SHBG provided areas under of the curve forreceived operator curves combinations of these markers were in the orderof 0.75 to 0.81. These combinations included higher levels of thefollowing throughout: THBG, CO8G, BTD, CO5, and ZPI.

Profilin 1 (PFN1) has been associated with inflammation and insulinresistance, and notably significant differences were detected bothbefore and after seroconversion (decreasing in the latter case). Notablythe a peak in profilin 1 was observed before seroconversion and pathwayanalysis of the differentially abundant proteins detected at this timeperiod indicated functionally related proteins (FIG. 7). Indeed ROCcharacteristics of the combination of PROF1 and FLNA improved the AUC to0.88. Collectively these findings reflect metabolic differences andchanges preceding the diagnosis of type 1 diabetes.

The invention claimed is:
 1. A method of determining an individual atrisk of and/or progression towards Type 1 Diabetes (T1D), the methodcomprising: a) determining a protein marker profile in a sample obtainedfrom the individual, said profile comprising afamin (AFAM) andapolipoprotein C-IV (APOC4); b) comparing the determined protein markerprofile of APOC4 and AFAM of the individual with that of a correspondingcontrol profile of APOC4 and AFAM in a control sample from a controlindividual or a pool of control individuals, c) responsive to thecomparison, determining the risk of and/or progression towards T1D inthe individual, wherein a lower and a higher level of APOC4 and AFAM,respectively, in the sample obtained from the individual as compared tothe levels of APOC4 and AFAM in the control sample indicates that theindividual is at risk of and/or progression towards T1D, and d)providing a treatment or dietary change to the individual to prevent T1Ddevelopment.
 2. The method according to claim 1, wherein the individualis determined at risk of and/or progressing towards T1D prior toseroconversion in the individual.
 3. The method according to claim 1,wherein the individual has a Human Leukocyte Antigen-conferred(HLA-conferred) risk of T1D.
 4. The method according to claim 1, furthercomprising: determining one or more protein markers selected from thegroup consisting of sex hormone-binding globulin (SHBG), adiponectin(ADIPO), thyroxine-binding globulin (THBG), apolipoprotein C-II (APOC2),insulin-like growth factor-binding protein 2 (IBP2), apolipoprotein C-I(APOC1), apolipoprotein C-III (APOC3), fetuin-B (FETUB), serum amyloidP-component (SAMP), clusterin (CLUS), C4b-binding protein alpha chain(C4BPA), C4b-BIRCH, binding protein beta chain (C4BPB), complementfactor I (CFAI), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4),insulin-like growth factor-binding protein 3 (IBP3), Serum amyloid A4protein (SAA4), complement component C8 alpha chain (CO8A), complementC1q subcomponent subunit B (C1QB), hyaluronan-binding protein 2 (HABP2),complement component C8 gamma chain (CO8G), transforming growthfactor-beta-induced protein ig-h3 (BGH3), ectonucleotidepyrophosphatase/phosphodiesterase family member 2 (ENPP2), poliovirusreceptor (PVR), vinculin (VINC), N-acetylmuramoyl-L-alanine amidase(PGRP2), contactin-1 (CNTN1), L-lactate dehydrogenase B chain 46 (LDHB),extracellular superoxide dismutase [Cu—Zn] (SODE), apolipoprotein A-IV(APOA4), neural cell adhesion and molecule 1 (NCAM1), and comparing thedetermined one or more protein markers with that of correspondingcontrol markers, wherein a lower level of SHBG, ADIPO, THBG, APOC2,APOC1, APOC3, BGH3, ENPP2, PVR, VINC, PGRP2, CNTN1, LDHB, SODE, APOA4,NCAM1, or IBP2 in the sample obtained from the individual as compared tothe levels in the control sample indicates that the individual is atrisk of and/or progression towards T1D, and wherein a higher level ofFETUB, SAMP, CLUS, C4BPA, C4BPB, CFAI, ITIH4, IBP3, SAA4, CO8A, C1QB,HABP2, or CO8G in the sample obtained from the individual as compared tothe levels in the control sample indicates that the individual is atrisk of and/or progression towards T1D.
 5. The method according to claim1, wherein said a)-c) steps are performed by a processor of a computingdevice.
 6. The method according to claim 1, wherein said sample isselected from the group consisting of a whole blood sample, a plasmasample, a serum sample, a sample comprising purified blood cells, atissue sample, and a urine sample.